Method and apparatus for neuropsychological modeling of human experience and purchasing behavior

• US 8,489,527 B2
• Filed: 10/21/2011
• Issued: 07/16/2013
• Est. Priority Date: 10/21/2010
• Status: Expired due to Fees

• Current Assignee: Holybrain Bvba
• Original Assignee: Holybrain Bvba
• Inventors: van Coppenolle, Bart, Vandormael, Philip W. J.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): PELLETT, DANIEL T
• Application Number:
  US13/278,789
• Publication Number:
  US 20120101966A1
• Time in Patent Office: 634 Days
• Field of Search: None
• US Class Current: 706/20
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: H04L 12/00 Data switching networks interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units G06F13/00
• CPC Class Codes: H04L 63/10 for controlling access to devices or network resources
• CPC Class Codes: H04L 65/61 for supporting one-way streaming services, e.g. Internet radio
• CPC Class Codes: H04L 65/65 Network streaming protocols, e.g. real-time transport protocol [RTP] or real-time control protocol [RTCP]
• CPC Class Codes: H04N 21/2542 for selling goods, e.g. TV shopping payment schemes, payment architectures or payment protocols for electronic shopping systems G06Q20/12
• CPC Class Codes: H04N 21/4826 using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02

Abstract

A system for accurately modeling of buyer/purchaser psychology and ranking of content objects within a channel for user initiated browsing and presentation contains a neuropsychological modeling engine, a ranking application, and a behavior modeler which communicate with each other and a presentation system over communication networks. The neuropsychological modeling engine utilizes metafiles associated with content objects, a purchaser/viewer model and a channel model to derive a value ψ representing an individual'"'"'s mood and a value m representing an individual'"'"'s motivational strength to select a content object. If the value ψ is within an acceptable predetermined range, the value m is used to determine a ranking for the content object relative to other content objects associated with the channel model. Also disclosed are a system and technique for simultaneously presenting multiple, s content object data streams on the user interface in a manner which encourages multidimensional browsing using traditional navigation commands.

Claims

1. A recommendation system operatively coupled to a network accessible source of indexed content objects and a viewer system, the recommendation system capable of modeling buyer/purchaser psychology, the recommendation system comprising:

A) a neuropsychological modeling engine operatively coupled to the network accessible source of indexed content objects;

B) a behavior modeler operatively coupled to the viewer system;

C) a ranking application operatively coupled to the neuropsychological modeling engine;

D) a first memory operatively coupled to the ranking application, the neuropsychological modeling engine, and the behavior modeler for storing a plurality of viewer models;

E) a second memory operatively coupled to the ranking application, the neuropsychological modeling engine, and the behavior modeler for storing a plurality of channel models and rankings of content objects relative to a channel model;

wherein the neuropsychological modeling engine is configured to;

i) generate a fear vector value having magnitude and direction parameters associated therewith and representing a viewer'"'"'s fear to select or purchase a content object received from the source of indexed content objects,

ii) generate a desire vector value having magnitude and direction parameters associated therewith and representing the viewer'"'"'s desire to select or purchase the content object,

iii) derive, from the desire vector value and the fear vector value, an emotion vector value having magnitude and direction parameters associated therewith,

iv) compare metadata associated with the content object to metadata associated with a viewer model and at least one viewer channel associated with the viewer, and

v) determine if the received content object is eligible for ranking amongst other content objects associated with the viewer channel in accordance with a viewer'"'"'s emotional motivation to select the content object; and

F) a model of psychology space stored in memory and accessible by the neuropsychological modeling engine, said model comprising a representation of a brain cortex having a plurality of emotions associated with portions thereof;

wherein the emotion vector value as mapped relative to the psychology space model represents the viewer'"'"'s mood and emotional motivation to select the content object.

2. The system of claim 1 wherein iii) further comprises:

iii(a)) derive, from the desire vector value and the fear vector value, a value ψ

representing a viewer'"'"'s mood;

iii(b)) derive, from the desire vector value and the fear vector value, a value m representing a viewer'"'"'s motivational strength to select the content object; and

wherein neuropsychological modeling engine is further configured to

v) if the value ψ

representing an viewer'"'"'s mood is within an acceptable predetermined range, providing the value m to ranking application.

3. The system of claim 2 wherein the ranking application is configured to, using the value m received from the neuropsychological modeling engine, determine a ranking for the content object relative to other content objects associated with the channel model.

4. A method for modeling of buyer/purchaser psychology comprising:

A) comparing metadata associated with a content object to metadata associated with a channel model;

B) generating a fear vector value having magnitude and direction parameters associated therewith and representing an individual'"'"'s fear to select or purchase the content object;

C) generating a desire vector value having magnitude and direction parameters associated therewith and representing the individual'"'"'s desire to select or purchase the content object; and

D) deriving from the value for the desire vector and the value for the fear vector a ranking for the content object relative to other content objects associated with the channel model;

E) storing, in memory accessible by the neuropsychological modeling engine, a psychology space model comprising a representation of a brain cortex having a plurality of emotions associated with portions thereof; and

F) mapping, relative to the psychology space model, an emotion vector value derived from the desire vector value and the fear vector value, a position of the emotion vector value relative to the psychology space model indicating an individual'"'"'s mood and emotional motivation to select the content object.

5. The method of claim 4 wherein D) comprises:

D1) deriving, from the desire vector value and the fear vector value, a value ψ

representing an individual'"'"'s mood.

6. The method of claim 5 wherein D) further comprises:

D2) deriving, from the desire vector value and the fear vector value, a value m representing an individual'"'"'s motivational strength to select or purchase the content object.

7. The method of claim 6 wherein D) further comprises:

D3) if the value ψ

representing an individuals mood is within an acceptable predetermined range, using the value m representing an individual'"'"'s motivational strength to select or purchase the content object to determine a ranking for the content object relative to other content objects associated with the channel model.

8. The method of claim 4 further comprising:

G) maintaining the channel model and a viewer model associated with the individual in a network accessible memory.

9. A system for modeling of buyer/purchaser psychology comprising:

A) a first network accessible memory for storing at least one channel model;

B) a modeling engine operably coupled to the network accessible memory and configured to compare metadata associated with a content object to metadata associated with the channel model and for generating;

i) a fear vector value having magnitude and direction parameters associated therewith and representing an individual'"'"'s fear to select or purchase the content object; and

ii) a desire vector value having magnitude and direction parameters associated therewith and representing the individual'"'"'s desire to select the content object; and

C) a model of psychology space stored in memory and accessible by the modeling engine, said model comprising at least a two-dimensional representation of a brain cortex;

wherein the fear and desire vector values comprise constituent components of an emotion vector value capable of being mapped onto the psychology space model, a position of the emotion vector value relative to the psychology space model indicating an individual'"'"'s mood and emotional motivation to select the content object.

10. The system of claim 9 wherein the modeling engine is further configured to generate:

iii) a value ψ

representing an individual'"'"'s mood, the value ψ

being derived from the desire vector value and the fear vector value.

11. The system of claim 10 wherein the modeling engine is further configured to generate:

iv) a value m representing an individual'"'"'s motivational strength to select or purchase the content object, the value m being derived from the desire vector value and the fear vector value.

12. The system of claim 9 further comprising:

D) a ranking module, responsive to the value m generated by the modeling engine, for deriving a ranking for the content object relative to other content objects associated with the channel model, if the value ψ

generated by the modeling engine is within an acceptable predetermined range.

13. The system of claim 9 further comprising:

D) a second network accessible memory for storing at least a portion of the content object and at least a portion of other content objects.

14. The system of claim 9 further comprising:

D) a network accessible data structure stored in memory, the data structure comprising any of;

channel identification data;

viewer identification data;

at least one dominant preference variable;

at least one sub-dominant preference variable;

or

memory reference identifying the location of a data structure representing rankings of content objects associated with the channel.